ECG classification using Deep CNN and Gramian Angular Field

TL;DR

This paper introduces a novel ECG classification approach using Gramian Angular Field transformation of signals into images, combined with CNNs, achieving high accuracy and improved interpretability for cardiovascular anomaly detection.

Contribution

It presents a new feature representation method for ECG signals that enhances classification accuracy and interpretability over existing techniques.

Findings

Achieved 97.47% and 98.65% accuracy in anomaly detection.

Improved classification performance compared to state-of-the-art methods.

Enabled visualization of temporal ECG patterns for better diagnosis.

Abstract

This paper study provides a novel contribution to the field of signal processing and DL for ECG signal analysis by introducing a new feature representation method for ECG signals. The proposed method is based on transforming time frequency 1D vectors into 2D images using Gramian Angular Field transform. Moving on, the classification of the transformed ECG signals is performed using Convolutional Neural Networks (CNN). The obtained results show a classification accuracy of 97.47% and 98.65% for anomaly detection. Accordingly, in addition to improving the classification performance compared to the state-of-the-art, the feature representation helps identify and visualize temporal patterns in the ECG signal, such as changes in heart rate, rhythm, and morphology, which may not be apparent in the original signal. This has significant implications in the diagnosis and treatment of cardiovascular diseases and detection of anomalies.